{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) MONAI Consortium  \n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
    "you may not use this file except in compliance with the License.  \n",
    "You may obtain a copy of the License at  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \n",
    "Unless required by applicable law or agreed to in writing, software  \n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
    "See the License for the specific language governing permissions and  \n",
    "limitations under the License.\n",
    "\n",
    "# 3D classification example based on DenseNet\n",
    "\n",
    "This tutorial shows an example of 3D classification task based on DenseNet and array format transforms.\n",
    "\n",
    "Here, the task is given to classify MR images into male/female.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_classification/torch/densenet_training_array.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.4.dev2426\n",
      "Numpy version: 1.26.4\n",
      "Pytorch version: 2.3.1+cu121\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: d622a16f927841fdd7d057b7553805405f0805e4\n",
      "MONAI __file__: /home/<username>/anaconda3/envs/monai/lib/python3.9/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.11\n",
      "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "Nibabel version: 5.2.1\n",
      "scikit-image version: 0.24.0\n",
      "scipy version: 1.13.1\n",
      "Pillow version: 10.4.0\n",
      "Tensorboard version: 2.17.0\n",
      "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "TorchVision version: 0.18.1+cu121\n",
      "tqdm version: 4.66.4\n",
      "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "psutil version: 6.0.0\n",
      "pandas version: 2.2.2\n",
      "einops version: 0.8.0\n",
      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "mlflow version: 2.14.2\n",
      "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "clearml version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import os\n",
    "import sys\n",
    "import shutil\n",
    "import tempfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import numpy as np\n",
    "\n",
    "import monai\n",
    "from monai.apps import download_and_extract\n",
    "from monai.config import print_config\n",
    "from monai.data import DataLoader, ImageDataset\n",
    "from monai.transforms import (\n",
    "    EnsureChannelFirst,\n",
    "    Compose,\n",
    "    RandRotate90,\n",
    "    Resize,\n",
    "    ScaleIntensity,\n",
    ")\n",
    "\n",
    "pin_memory = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/chyang/Documents/monai_tutorials_gitee/data\n"
     ]
    }
   ],
   "source": [
    "# Set data directory\n",
    "os.environ['MONAI_DATA_DIRECTORY'] = '/home/chyang/Documents/monai_tutorials_gitee/data'\n",
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "if directory is not None:\n",
    "    os.makedirs(directory, exist_ok=True)\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/\n",
    "images = [\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI314-IOP-0889-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI249-Guys-1072-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI609-HH-2600-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI173-HH-1590-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI020-Guys-0700-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI342-Guys-0909-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI134-Guys-0780-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI577-HH-2661-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI066-Guys-0731-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI130-HH-1528-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI607-Guys-1097-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI175-HH-1570-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI385-HH-2078-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI344-Guys-0905-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI409-Guys-0960-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI584-Guys-1129-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI253-HH-1694-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI092-HH-1436-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI574-IOP-1156-T1.nii.gz\"]),\n",
    "    os.sep.join([root_dir, \"ixi\", \"IXI585-Guys-1130-T1.nii.gz\"]),\n",
    "]\n",
    "\n",
    "# 2 binary labels for gender classification: man or woman\n",
    "labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0])\n",
    "\n",
    "# Represent labels in one-hot format for binary classifier training,\n",
    "# BCEWithLogitsLoss requires target to have same shape as input\n",
    "labels = torch.nn.functional.one_hot(torch.as_tensor(labels)).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ixi.tar: 100%|██████████| 4.51G/4.51G [10:39<00:00, 7.57MB/s]    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-07-15 19:35:01,358 - INFO - Downloaded: /home/chyang/Documents/monai_tutorials_gitee/data/ixi.tar\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-07-15 19:35:07,112 - INFO - Verified 'ixi.tar', md5: 34901a0593b41dd19c1a1f746eac2d58.\n",
      "2024-07-15 19:35:07,112 - INFO - Writing into directory: /home/chyang/Documents/monai_tutorials_gitee/data/ixi.\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(images[0]):\n",
    "    resource = \"http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI-T1.tar\"\n",
    "    md5 = \"34901a0593b41dd19c1a1f746eac2d58\"\n",
    "\n",
    "    dataset_dir = os.path.join(root_dir, \"ixi\")\n",
    "    tarfile_name = f\"{dataset_dir}.tar\"\n",
    "\n",
    "    download_and_extract(resource, tarfile_name, dataset_dir, md5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'monai.data.meta_tensor.MetaTensor'> torch.Size([3, 1, 96, 96, 96]) tensor([[1., 0.],\n",
      "        [1., 0.],\n",
      "        [1., 0.]]) torch.Size([3, 2])\n"
     ]
    }
   ],
   "source": [
    "# Define transforms\n",
    "train_transforms = Compose([ScaleIntensity(), EnsureChannelFirst(), Resize((96, 96, 96)), RandRotate90()])\n",
    "\n",
    "val_transforms = Compose([ScaleIntensity(), EnsureChannelFirst(), Resize((96, 96, 96))])\n",
    "\n",
    "# Define nifti dataset, data loader\n",
    "check_ds = ImageDataset(image_files=images, labels=labels, transform=train_transforms)\n",
    "check_loader = DataLoader(check_ds, batch_size=3, num_workers=2, pin_memory=pin_memory)\n",
    "\n",
    "im, label = monai.utils.misc.first(check_loader)\n",
    "print(type(im), im.shape, label, label.shape)\n",
    "\n",
    "# create a training data loader\n",
    "train_ds = ImageDataset(image_files=images[:10], labels=labels[:10], transform=train_transforms)\n",
    "train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2, pin_memory=pin_memory)\n",
    "\n",
    "# create a validation data loader\n",
    "val_ds = ImageDataset(image_files=images[-10:], labels=labels[-10:], transform=val_transforms)\n",
    "val_loader = DataLoader(val_ds, batch_size=2, num_workers=2, pin_memory=pin_memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "epoch 1/5\n",
      "1/5, train_loss: 0.7100\n",
      "2/5, train_loss: 0.6616\n",
      "3/5, train_loss: 0.6717\n",
      "4/5, train_loss: 0.8146\n",
      "5/5, train_loss: 0.6166\n",
      "epoch 1 average loss: 0.6949\n",
      "----------\n",
      "epoch 2/5\n",
      "1/5, train_loss: 0.4720\n",
      "2/5, train_loss: 0.2411\n",
      "3/5, train_loss: 0.7586\n",
      "4/5, train_loss: 0.5767\n",
      "5/5, train_loss: 1.4000\n",
      "epoch 2 average loss: 0.6897\n",
      "saved new best metric model\n",
      "Current epoch: 2 current accuracy: 0.6000 \n",
      "Best accuracy: 0.6000 at epoch 2\n",
      "----------\n",
      "epoch 3/5\n",
      "1/5, train_loss: 0.2712\n",
      "2/5, train_loss: 0.9535\n",
      "3/5, train_loss: 0.5606\n",
      "4/5, train_loss: 1.1037\n",
      "5/5, train_loss: 0.4614\n",
      "epoch 3 average loss: 0.6701\n",
      "----------\n",
      "epoch 4/5\n",
      "1/5, train_loss: 0.4613\n",
      "2/5, train_loss: 0.5903\n",
      "3/5, train_loss: 0.2739\n",
      "4/5, train_loss: 0.4269\n",
      "5/5, train_loss: 0.3930\n",
      "epoch 4 average loss: 0.4291\n",
      "Current epoch: 4 current accuracy: 0.6000 \n",
      "Best accuracy: 0.6000 at epoch 2\n",
      "----------\n",
      "epoch 5/5\n",
      "1/5, train_loss: 0.3852\n",
      "2/5, train_loss: 0.5978\n",
      "3/5, train_loss: 0.2674\n",
      "4/5, train_loss: 1.3506\n",
      "5/5, train_loss: 0.6291\n",
      "epoch 5 average loss: 0.6460\n",
      "Training completed, best_metric: 0.6000 at epoch: 2\n"
     ]
    }
   ],
   "source": [
    "# Create DenseNet121, CrossEntropyLoss and Adam optimizer\n",
    "model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)\n",
    "\n",
    "loss_function = torch.nn.CrossEntropyLoss()\n",
    "# loss_function = torch.nn.BCEWithLogitsLoss()  # also works with this data\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "\n",
    "# start a typical PyTorch training\n",
    "val_interval = 2\n",
    "best_metric = -1\n",
    "best_metric_epoch = -1\n",
    "epoch_loss_values = []\n",
    "metric_values = []\n",
    "writer = SummaryWriter()\n",
    "max_epochs = 5\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    print(\"-\" * 10)\n",
    "    print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    step = 0\n",
    "\n",
    "    for batch_data in train_loader:\n",
    "        step += 1\n",
    "        inputs, labels = batch_data[0].to(device), batch_data[1].to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = loss_function(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_len = len(train_ds) // train_loader.batch_size\n",
    "        print(f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\")\n",
    "        writer.add_scalar(\"train_loss\", loss.item(), epoch_len * epoch + step)\n",
    "\n",
    "    epoch_loss /= step\n",
    "    epoch_loss_values.append(epoch_loss)\n",
    "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        model.eval()\n",
    "\n",
    "        num_correct = 0.0\n",
    "        metric_count = 0\n",
    "        for val_data in val_loader:\n",
    "            val_images, val_labels = val_data[0].to(device), val_data[1].to(device)\n",
    "            with torch.no_grad():\n",
    "                val_outputs = model(val_images)\n",
    "                value = torch.eq(val_outputs.argmax(dim=1), val_labels.argmax(dim=1))\n",
    "                metric_count += len(value)\n",
    "                num_correct += value.sum().item()\n",
    "\n",
    "        metric = num_correct / metric_count\n",
    "        metric_values.append(metric)\n",
    "\n",
    "        if metric > best_metric:\n",
    "            best_metric = metric\n",
    "            best_metric_epoch = epoch + 1\n",
    "            torch.save(model.state_dict(), \"best_metric_model_classification3d_array.pth\")\n",
    "            print(\"saved new best metric model\")\n",
    "\n",
    "        print(f\"Current epoch: {epoch+1} current accuracy: {metric:.4f} \")\n",
    "        print(f\"Best accuracy: {best_metric:.4f} at epoch {best_metric_epoch}\")\n",
    "        writer.add_scalar(\"val_accuracy\", metric, epoch + 1)\n",
    "\n",
    "print(f\"Training completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\")\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Occlusion sensitivity\n",
    "One method for trying to visualise why the network made a given prediction is occlusion sensitivity. We occlude part of the image, and see how the probability of a given prediction changes. We then iterate over the image, moving the occluded portion as we go, and in doing so we build up a sensitivity map detailing which areas were the most important in making the decision.\n",
    "\n",
    "#### Bounds\n",
    "If we were to test the occlusion centred on all voxels in our image, we would have to do `torch.prod(im.shape) = 96^3 = ~1e6` predictions. We can use the bounding box to only to the estimations in a region of interest, for example over one slice.\n",
    "\n",
    "To do this, we simply give the bounding box as `(minC,maxC,minD,maxD,minH,maxH,minW,maxW)`. We can use `-1` for any value to use its full extent (`0` and `im.shape-1` for min's and max's, respectively).\n",
    "\n",
    "#### Output\n",
    "The output image in this example will look fairly bad, since our network hasn't been trained for very long. Training for longer should improve the quality of the occlusion map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a validation data loader\n",
    "test_ds = ImageDataset(image_files=images[-10:], labels=labels[-10:], transform=val_transforms)\n",
    "test_loader = DataLoader(val_ds, batch_size=1, num_workers=2, pin_memory=torch.cuda.is_available())\n",
    "itera = iter(test_loader)\n",
    "\n",
    "\n",
    "def get_next_im():\n",
    "    test_data = next(itera)\n",
    "    return test_data[0].to(device), test_data[1].unsqueeze(0).to(device)\n",
    "\n",
    "\n",
    "def plot_occlusion_heatmap(im, heatmap):\n",
    "    plt.subplots(1, 2)\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.imshow(np.squeeze(im.cpu()))\n",
    "    plt.colorbar()\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.imshow(heatmap)\n",
    "    plt.colorbar()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a random image and its corresponding label\n",
    "img, label = get_next_im()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:00<00:00, 15.08it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the occlusion sensitivity map\n",
    "occ_sens = monai.visualize.OcclusionSensitivity(nn_module=model, mask_size=12, n_batch=10)\n",
    "# Only get a single slice to save time.\n",
    "# For the other dimensions (channel, width, height), use\n",
    "# -1 to use 0 and img.shape[x]-1 for min and max, respectively\n",
    "depth_slice = img.shape[2] // 2\n",
    "occ_sens_b_box = [depth_slice - 1, depth_slice, -1, -1, -1, -1]\n",
    "\n",
    "occ_result, _ = occ_sens(x=img, b_box=occ_sens_b_box)\n",
    "occ_result = occ_result[0, label.argmax().item()][None]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(25, 15), facecolor=\"white\")\n",
    "\n",
    "for i, im in enumerate([img[:, :, depth_slice, ...], occ_result]):\n",
    "    cmap = \"gray\" if i == 0 else \"jet\"\n",
    "    ax = axes[i]\n",
    "    im_show = ax.imshow(np.squeeze(im[0][0].detach().cpu()), cmap=cmap)\n",
    "    ax.axis(\"off\")\n",
    "    fig.colorbar(im_show, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
